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Model: 24-mohamedyehia/Gloss2Text-V1-Gemma3-270M
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- 24-mohamedyehia/gloss2text-Ar-sft
+language:
+- ar
+base_model:
+- google/gemma-3-270m-it
+pipeline_tag: text-generation
+tags:
+- sign-language
+- arabic
+- ArSL
+- gloss-to-text
+- gemma-3
+- accessibility
+---
+
+# Gloss2Text-V1-Gemma3-270M (Merged)
+
+## Overview
+**Gloss2Text-V1-Gemma3-270M** is a fine-tuned version of Google's **Gemma-3-270m-it**, optimized for **Arabic Sign Language (ArSL) gloss-to-text translation**.
+
+This release is **merged**, which means the LoRA adapters have already been fused into the base model weights for faster inference and easier plug-and-play usage.
+
+## Model Description
+- **Task:** Converts a sequence of Arabic glosses into a natural, grammatically correct Modern Standard Arabic (MSA) sentence.
+- **Input format:** Arabic gloss text, for example: "أنا شرب ماء الآن"
+- **Output format:** Clean MSA text, for example: "أنا أشرب الماء الآن."
+- **Architecture:** Gemma-3, 270M parameters
+- **Training method:** Supervised fine-tuning with LoRA (rank 64), then merged
+
+## Training Highlights
+The model was trained on a specialized dataset containing diverse ArSL gloss-sentence pairs.
+- **Final eval loss:** ~0.34
+- **Precision:** Trained with `bf16` for improved numerical stability
+
+
+## How to Use
+Because this is a merged model, you can load it directly with the `transformers` library without needing `peft`:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "24-mohamedyehia/Gloss2Text-V1-Gemma3-270M"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+def translate_gloss(gloss_text):
+    prompt = f"Translate ArSL gloss to an MSA sentence.\nGloss: {gloss_text}\nOutput: "
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    outputs = model.generate(**inputs, max_new_tokens=50)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+
+# Example
+print(translate_gloss("أنا ذهاب صيدلية"))
+```
+
+
+## Developer
+
+Developed by **Mohamed Yehia**.
+
+- **LinkedIn:** [Mohamed Yehia](https://www.linkedin.com/in/24-mohamed-yehia/)